In graph theory, a branch of mathematics, the handshaking lemma is the statement that every finite undirected graph has an even number of vertices with odd degree (the number of edges touching the vertex). In more colloquial terms, in a party of people some of whom shake hands, an even number of people must have shaken an odd number of other people's hands. The vertices of odd degree in a graph are sometimes called odd nodes or odd vertices; in this terminology, the handshaking lemma can be restated as the statement that every graph has an even number of odd nodes.
The handshaking lemma is a consequence of the degree sum formula (also sometimes called the handshaking lemma),
Other applications include proving that for certain combinatorial structures, the number of structures is always even, and assisting with the proofs of Sperner's lemma and the mountain climbing problem. The complexity class PPA encapsulates the difficulty of finding a second odd vertex, given one such vertex in a large implicitly-defined graph.
Euler's proof of the degree sum formula uses the technique of double counting: he counts the number of incident pairs (v,e) where e is an edge and vertex v is one of its endpoints, in two different ways. Vertex v belongs to deg(v) pairs, where deg(v) (the degree of v) is the number of edges incident to it. Therefore, the number of incident pairs is the sum of the degrees. However, each edge in the graph belongs to exactly two incident pairs, one for each of its endpoints; therefore, the number of incident pairs is 2|E|. Since these two formulas count the same set of objects, they must have equal values.
In a sum of integers, the parity of the sum is not affected by the even terms in the sum; the overall sum is even when there is an even number of odd terms, and odd when there is an odd number of odd terms. Since one side of the degree sum formula is the even number 2|E|, the sum on the other side must have an even number of odd terms; that is, there must be an even number of odd-degree vertices.
Alternatively, it is possible to use mathematical induction to prove that the number of odd-degree vertices is even, by removing one edge at a time from a given graph and using a case analysis on the degrees of its endpoints to determine the effect of this removal on the parity of the number of odd-degree vertices.
In special classes of graphs
The degree sum formula implies that every r-regular graph with n vertices has nr/2 edges. In particular, if r is odd then the number of edges must be divisible by r, and the number of vertices must be even.
The handshaking lemma does not apply to infinite graphs, even when they have only a finite number of odd-degree vertices. For instance, an infinite path graph with one endpoint has only a single odd-degree vertex rather than having an even number of such vertices.
Euler paths and tours
Leonhard Euler first proved the handshaking lemma in his work on the Seven Bridges of Königsberg, asking for a walking tour of the city of Königsberg (now Kaliningrad) crossing each of its seven bridges once. This can be translated into graph-theoretic terms as asking for an Euler path or Euler tour of a connected graph representing the city and its bridges: a walk through the graph that traverses each edge once, either ending at a different vertex than it starts in the case of an Euler path or returning to its starting point in the case of an Euler tour. Euler stated the fundamental results for this problem in terms of the number of odd vertices in the graph, which the handshaking lemma restricts to be an even number. If this number is zero, an Euler tour exists, and if it is two, an Euler path exists. Otherwise, the problem cannot be solved. In the case of the Seven Bridges of Königsberg, the graph representing the problem has four odd vertices, and has neither an Euler path nor an Euler tour.
In the Christofides–Serdyukov algorithm for approximating the traveling salesperson problem, the fact that the number of odd vertices is even plays a key role, allowing the algorithm to connect those vertices in pairs in order to construct a graph on which an Euler tour forms an approximate TSP tour.
In combinatorial enumeration
Several combinatorial structures may be shown to be even in number by relating them to the odd vertices in an appropriate "exchange graph".
For instance, as C. A. B. Smith proved, in any cubic graph G there must be an even number of Hamiltonian cycles through any fixed edge uv; Thomason (1978) used a proof based on the handshaking lemma to extend this result to graphs G in which all vertices have odd degree. Thomason defines an exchange graph H, the vertices of which are in one-to-one correspondence with the Hamiltonian paths beginning at u and continuing through v. Two such paths p1 and p2 are connected by an edge in H if one may obtain p2 by adding a new edge to the end of p1 and removing another edge from the middle of p1; this is a symmetric relation, so H is an undirected graph. If path p ends at vertex w, then the vertex corresponding to p in H has degree equal to the number of ways that p may be extended by an edge that does not connect back to u; that is, the degree of this vertex in H is either deg(w) − 1 (an even number) if p does not form part of a Hamiltonian cycle through uv, or deg(w) − 2 (an odd number) if p is part of a Hamiltonian cycle through uv. Since H has an even number of odd vertices, G must have an even number of Hamiltonian cycles through uv.
In connection with the exchange graph method for proving the existence of combinatorial structures, it is of interest to ask how efficiently these structures may be found. For instance, suppose one is given as input a Hamiltonian cycle in a cubic graph; it follows from Smith's theorem that there exists a second cycle. How quickly can this second cycle be found? Papadimitriou (1994) investigated the computational complexity of questions such as this, or more generally of finding a second odd-degree vertex when one is given a single odd vertex in a large implicitly-defined graph. He defined the complexity class PPA to encapsulate problems such as this one; a closely related class defined on directed graphs, PPAD, has attracted significant attention in algorithmic game theory because computing a Nash equilibrium is computationally equivalent to the hardest problems in this class.
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